A user built an AI-assisted animation pipeline using Blender and ComfyUI, employing LTX 2.3 as an alternative render engine. They modified the official IC-LoRA workflow to include both first and last frame conditioning, and used a custom control video combining depth and AO passes from Blender. With only the distilled LoRA, motion and composition were stable but textures faded away from guide frames; adding IC-LoRA preserved textures but caused composition drift, character misplacement, and overall instability. The user seeks a technical understanding of how IC-LoRA interacts with the distilled model and guide frames to explain this trade-off.
A follow-up comparison tested Krea2 Turbo model with and without three different de-censor LoRAs. Two tiny LoRAs (about 200 bytes, changing only 2–3 weights) solely bypass the model's censorship filters without adding new styles or concepts. Results demonstrate that the built-in filters significantly nerf SFW image generation for facial expressions, bruises, body builds, emotions, and other natural features. The bypass LoRAs restore these lost capabilities by freeing concepts the model already possesses but underutilizes due to filtering. A trained SNOFS LoRA also removes filters but introduces additional styles, making it a less controlled comparison. The study uses fixed prompts and standardized parameters for fair evaluation.
The (Deno) Local LLM Loader is a new ComfyUI node that connects local LLMs (Ollama, LM Studio, llama.cpp, vLLM, or any OpenAI-compatible local server) directly into image generation workflows. It enables tasks like turning short ideas into detailed prompts, generating prompts from images via vision-capable models, and chaining multiple LLM steps (e.g., draft → review → final cleanup). The accompanying (Deno) Local LLM Reviewer node can pass or block image outputs based on review text, allowing iterative refinement without leaving ComfyUI. Designed as a local-first tool, it requires no remote API keys and keeps models loaded for efficient prompt chains. The nodes are available through ComfyUI Manager under "Deno Custom Nodes" and on GitHub.
A new LoRA file, 'Krea2 Filter Bypass Fedor,' has been publicly released on Civitai that effectively disables the safety filter in Krea 2 diffusion transformer by overwriting only the two refusal-associated control knobs (9 and 10) while leaving all others at zero. Unlike earlier bypasses that modified multiple knobs and caused style warping or plasticized faces, this approach mathematically guarantees no style or anatomy drift because zeroed knobs cannot be moved by strength adjustments. The LoRA stacks cleanly with realism, character, and style LoRAs without interference, letting the base model's own quality shine through. A recommended strength of 3–5 defeats most refusals, and the author suggests switching to FilterBypass3 for any remaining knob-11-based refusals. The design was derived from a community vector analysis by u/piero_deckard, identifying the exact roles of each knob.
A new Blender workflow enables direct asset generation and 3D-scene-to-video conversion using two open-source tools. The Asset Generator (2D/3D) creates low-poly 3D assets inside Blender, removing the need for external asset libraries. The Pallaidium add-on then converts the generated 3D scene into a video, supporting AI video models like LTX and 3DREAL. Both tools are developed by tin2tin and available on GitHub. This integrated pipeline streamlines the process from asset creation to final video output entirely within Blender.
Researchers propose Elastic Diffusion Transformer (E-DiT), an adaptive framework to accelerate Diffusion Transformers by exploiting sample-dependent sparsity. Each DiT block is equipped with a lightweight router that dynamically decides to skip the block or reduce its MLP width. A training-free block-level feature caching mechanism further eliminates redundant computations. Experiments on Qwen-Image, FLUX, and Hunyuan3D-3.0 achieve up to ~2× speedup with negligible quality loss. Code and paper are publicly available.